Category: Big Data Analysis

The main task of big data analysis tools is to help a company collect, organize, and group data so that it can learn to extract useful information from it and monetize it. For many companies, it is essential that the manager can at any time request specific statistics from the system, conclude certain patterns and trends, and immediately use this information when making one or another management decision (data-driven decision-making approach).

Using big data analysis tools, you can clean and process data, store and manage information, and visualize analysis results. At the same time, high-quality work requires high-performance analytics – unique software and hardware solutions that provide predictive analytics, data mining, text analysis, and data storage optimization. Big data processing tools can quickly work with huge volumes of complex and dynamically changing information. They also scale well when using extensive data collection.

Where are Big Data tools used?

Solutions for Big Data analysis is actively used in a variety of sectors of the economy.

Thus, in medicine, technologies are used to predict treatment results, analyze CT and MRI images for the presence of pathologies, and identify patients from high-risk groups.

In retail, Big Data analysis tools are used to develop a competent marketing campaign (for example, when you need to understand your target audience, distribute customers into different groups, and formulate a key message in advertising or the USP of a product). In manufacturing, solutions are usually implemented simultaneously with the installation of specialized sensors and cameras in workshops, information from which is loaded in real time into a software tool for analytics. Further, the statistics produced by the machine allow engineers to plan more competently repairs, monitor compliance with safety regulations in production, and minimize downtime.

In the banking industry, tools for processing big data are used in many different ways, from scoring the customer base (when it is necessary to identify paying and creditworthy customers) to preventing fraudulent transactions (anti-fraud solutions allow anomalies in transactions to be recorded and reported in real-time bank employee). Solutions are also used to improve customer service, such as analyzing the workload of bank branches and checking complaints received from customers.

Tool Types

Each big data service can usually be classified into one or more categories depending on its functionality.

Storage And Management Tools. We are talking about databases that store large amounts of information, often in distributed networks. Two examples are NoSQL databases like MongoDB and Cassandra and the Hadoop Distributed File System (HDFS).

Processing tools sort, index, and mark up information for subsequent analysis. Examples are Apache Airflow and Apache NiFi.

Data Analysis Tools. These solutions are directly engaged in analytics—extracting valuable information from processed “raw materials.” Machine learning algorithms are frequently employed for this. For example, in Python, popular data analysis libraries include Pandas, Sklearn, CatBoost, PyTorch, TensorFlow, etc.

Visualization tools are used after the data has been prepared, and primary analytics have been carried out. Solutions are often presented in the form of convenient dashboards on which data is presented in an understandable format: in charts, graphs, or information panels. Examples: Superset, QlickSence, Tableau, etc.

Management and Security Tools. We must also remember the requirements of confidentiality and security, especially when it comes to personal data. This is where special tools come in handy. Examples: Talend and Varonis.

Streaming Tools. Sometimes, information needs to be processed in real-time so that the manager can use instant analytics. Examples: Apache Kafka and Apache Flink.

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